8 Burst results for "Frank Rosenblatt"

"frank rosenblatt" Discussed on American Innovations

American Innovations

08:12 min | 3 months ago

"frank rosenblatt" Discussed on American Innovations

"American innovations is brought to you by soulmates. AMC's new series from the emmy winning writer of Black Mirror and stranger things set fifteen years in the future soulmates till six different stories were a life changing scientific discovery gives people the ability to uncover their one soulmate. If you could take a test to find them, would you what if you were married? What if they were a world away? No choice is free from life's deepest consequences Mullen Ackerman. Charlie Heaton Sarah Snook and Bill Skarsgard. Star in soulmates premiering. Monday. October fifth at ten PM on AMC at yen is the future proof payments platform that helps. Your Business grow from online in-app purchases to touch free in store payments at yen makes it easy to accept payments all on one powerful platform with Adnan. Your Business has the flexibility to accept any payment so you can connect with any customer. No matter how they WANNA pay at yen's technology also gives you in depth insights, revenue optimization, real time reporting and more. So you can keep your customers happy and your business growing at yen built their payments platform to help businesses like yours get ahead and stay ahead start your journey today at ad yen dot com. That's a D. Y. E. N. dot com. Business not boundaries. From wonder, I'm Steven Johnson and this is American innovations. This is the fourth episode in our series about artificial intelligence and the thinking machine. and. The first three episodes we follow the journey of computer scientists who dreamed of breathing the best of human intellectual abilities into machines. We followed the epic years long battle between IBM chess world champion, Garry, Casper up in our last episode explored how the Pentagon's investment in new technology eventually led to the creation of the iphones a personal assistant Siri. This episode is about a leap in the power of artificial intelligence that caused some to raise the alarm about possible dangers ahead. It's called machine learning and it began right around the time series launched. Late in two thousand eleven google the term and you will see pages of articles, forums, academic papers, research, and blogs about the topic. But at its essence, it boils down to this machine. Learning is providing information about the world or a problem to a computer. So it's understanding improves over time you can think of it being something like how a human can learn through practice or seeing examples over and over. It's an endeavor that excites the biggest names in tech. This is episode for I learn therefore I am. Early in two thousand eleven, Jeff Dean, a veteran programmer at Google is browsing one of the companies well-stocked snack kitchens. Their. SMOOTHIES. Fancy coffee. He gets talking with a quietspoken Stanford professor named Andrew Ing. The conversation will change the lives of both of them. And all of us to. So, Andrew, what are you working on these days? I'm running a new a high project at Google lacks. The media lexical secretive moonshot factory because no one knows where it is that sounds exciting. What's it about? Well, we're training neural networks you know computer systems kind of modeled on the human brain no way my undergrad thesis on neural networks back in one thousand, nine, hundred, I went out of fashion a long time ago because no one could get them to work very well I know I couldn't I think we are going to get them to. Work this time the basic ideas mostly the same, but it's just that we have so much more data now than we used to and much more powerful computers to train them on. So what are you teaching them to make sense of photos? We scrape together millions of images from Youtube to try and teach them to recognize images out there in the world you should pop by sometime we call it project Marvin after Marvin Minsk a I at Mit. Dean does drop by to visit Project Marvin, and before long he's spending a lot of time with Andrew Ing and a small team at Google lacks the idea of getting neural networks properly working after all these years peaks is curiosity and the way ing is going about it is perfect for dean. Dean is widely recognized as one of Silicon Valley's most creative and influential programmers. In the early two thousands he created the huge distributed software systems that harnessed together countless servers to power Google, search engine, and other services. Now, he's working with ing to get thousands of computers to work together to power neural networks. A few months after ING and Dean's Meeting Project. Marvin has been renamed Google brain and the team starts an experiment that will help change the path of Google and technology all over the planet. Involve only a few simple steps. I the Google brain team clips images at random from ten million youtube videos. Next. They feed them into neural network software running on sixteen thousand powerful processors with one billion connections between the simulated neurons, and then leave it running for a week to browse the images and try to find. Pavan. Return they discover the software has learned to detect the most common objects on Youtube all by itself, human faces and body parts and cats. On June twenty, fifth two, thousand, twelve, the results land on the front page of the New York. Times Business Section the headline is a riddle. How many computers to identify a Cap Sixteen thousand. Dean, tells the reporter that the team never told the computer what a cat was it came up with the concept all on its own much like a human it learned to identify something through visual repetition. The project convinces Google it needs to invest more a lot more into neural networks. One of the first people they turn to is Jeff Hidden Lean. Dry. witted British professor living in Canada. Hidden it helped keep alive the idea of neural networks for decades during which they didn't see much use at all. Way Back. In nineteen sixty psychologist Frank Rosenblatt had built an artificial neural network out of electronics called the mark one percent tron it took up almost an entire room and it was attached to a camera and could learn to distinguish different letters of the alphabet. Rosenblatt predicted future versions would identify aircraft for the military and take voice commands. But then Marvin mincy and mit the early Ai. Pioneer, we met in episode one wrote a book that claimed to prove neural networks would never be very powerful interests in progress on the idea mostly fizzled out apart from a few stubborn believers. Hinton had been obsessed with neural networks for a long time in the nineteen seventies his PhD supervisor at the University of Edinburgh advise the straggling haired hidden working on neural networks was a waste of time, but he refused to give up. Hinton was a firm believer that computers could learn and develop intuition not just processed the world using Bilton rules. Neural networks even garnered notice in popular culture in Nineteen ninety-two, the movie Terminator Two Judgment Day Arnold. Schwarzenegger. Playing the t eight, hundred robot gets quizzed about his brain by John Connor the kid he traveled back in time to save. Staff that you having been program with. You can be. You know. More. Human. That's all the time. My CPU is a neural net process of a learning computer. To.

Google Jeff Dean Andrew Ing AMC Youtube Marvin professor emmy Mullen Ackerman Charlie Heaton Sarah Snook Bill Skarsgard Frank Rosenblatt Black Mirror Marvin mincy IBM writer Steven Johnson
"frank rosenblatt" Discussed on Talking Machines

Talking Machines

12:19 min | 9 months ago

"frank rosenblatt" Discussed on Talking Machines

"You are listening to talking machines Catherine Gorman Lawrence and Neil. We are again taping an episode in front of a live audience digitally recorded though on on talking machines. And if you want to be part of our live. Studio audience big quotes. You can follow us on twitter at Ti Okay. N. G. M. C. H. S. Or hit us up on the talking machines at gmail.com and our guest today for this interview on talking. Machines is Dr Terence. Annouce key doctors and thank you so much for taking the time to join us today. I really appreciate it Great to be here so we ask all of our guests the same question I. How did you get where you are? What's been your academic and industrial journey. You're also very involved in the reps conference. Tell US everything well. A wise man once told me that careers are only made retrospectively and I have no idea how he got here. There was no plan. It went through a sequence of stages starting with graduate school at Princeton in theoretical physics. From there when I finished that I for reasons that have to do with the field of physics. At the time which was a little bit more bummed I went into neuroscience so that was a post doc and then from there that's when I met. Geoffrey Hinton and had changed my life because we met him at a small seminar here in San Diego and set nineteen seventy nine. We hit it off and From that over the next few years you know blossoms the the Boehner Sheen and back prop and you know. The rest was history. Terry who you post talking with where you post talking in San Diego no no. This was a post doc at Harvard. Medical School in the Department of Neurobiology with Stephen Kofler who was widely considered to be the founder of modern neurobiology and It was an experimental post. Doc I actually recorded from neurons. Subic seventy nine. You mentioning physics. It was a little bit more bond a in some sort of connection modeling. That was also a very quiet period. That wasn't a lot going on it. Was this sort of age of classical. Ai Right you're absolutely right. This was in fact. It was the neural network winter. The seventies and it was primarily because of the failure of the perception. That's neat because you say failure of the percents on I read about that a lot. Do you really did fail. All was the men's ski paper little. What the mid ski books are in Minsk. Eighty books have killed it but was it a fair representation. Well you know it's interesting. I think that that's the myth that that book killed it but I actually think that there are other things going on and and Rosenblatt had died as well which seems pretty significant. Yes well He. He was a pioneer. But you have to understand that digital computers were regally primitive back. Then you know that even the most expensive you know the biggest computers you could buy. Don't have the power of your wristwatch today. Rosenblatt actually had to build an analog device. It a million dollars in today's dollars to build a analog device that had potentially otters driven by motors for the weight sums the learning. Wasn't it potentially because you know digital computers? Were good at logic but they were terrible. Doing a floating point is amazing so he built that at Cornell. Right that's right yeah Funded by the owner. Any case by by the time that we were getting started computers was the vaccine era. It was becoming possible. Do Simulations You know they were small-scale by today's standards but but really meant we could explorer in a way that Frank Rosenblatt couldn't so what you're saying around the perceptual and so just forbid of context for Central and sixty one. Is that right? It was fifty nine. I think it was the book but you know it was in that era of early sixty zero and so then there's this period where the digital computer actually wasn't powerful enough to do much and then digital kind of overtook and divinity but these analog machines would just now impractical from a point of view of expense. So you're saying it's less the book and more of a shift to the Digital Machine. That in those early days wasn't powerful enough to simulate the perception. Yes so I I have you know. I have a feeling that history will show that A. I was like the blind man looking under the Lamppost. His keys and someone came along and said where did you lose your keys He said well somewhere else. But this is the only place right can see. I was reading Donald BACI quote. I recently At the beginning of his book about the I which is just a fascinating area and I guess he spent a lot of his career and he did work in in the wool on radar and he was talking about the Radio Club. Which is these early Cybernet assist and the potential of the analog or digital computer to be what represented the brain and his perspective was he. He was sure it wasn't a digital computer and he wasn't sure it was an analog computer either and he thought it was kind of somewhere in between but it feels like that in between is what you're saying is that was the difficult bit to look and perhaps a police were able to look now. That's right I you know. It's I think it's being driven. This is true all science that what you cannot understand is is really determined by the tools that you have for making measurements for doing simulations in it's really only this modern era that has given us enough tools both to make progress with understanding how the brain works and also with a because of the fact that we have a tremendous amount of power now but just to go back to that early era. I think you know I once asked L. Annual you know who is at Carnegie Mellon and it was a time when Geoff Hinton was an assistant professor and I was at Johns Hopkins and I you know he was at the first fifty six meeting at Dartmouth or a I was born and I I said well. Why was it that you didn't look at the brain and for for inspiration and he said well we did. But there wasn't very much known about the at the time to help us out so we just had make doing our own and he's right. That was a era. You know the the fifties was kind of the the beginning of what we now understand about the signals in the brain. Actually potential synoptic potentials. So you know in a sense. What what he was saying was that we basically use the tools we have available the time which was basically computers but what they were good at. What were they good at? They were good at logic at rules. A binary programming. So that you know that was In a sense they were forced to do that. That's a really. WanNa come back to nine hundred seventy nine in a moment but this is an interesting context to that because of course. Vena initially was someone who spread across. Both these areas of Norbert Vena who was at mit founded cybernetics spread across both these areas of the analog and digital he did his PhD thesis on Russell and Whitehead's book but one thing I was reading about recently is there was a big falling out between Vina. I'm McCulloch Pitts. And it's sort of interesting. That Vena wasn't there at the I. E. T. in fifty six and I sometimes wonder was that more about personalities and wanting this sort of old guard to stay away because you always feel veto with someone who who bridge these worlds it. You know that's the fascinating story. I actually wrote a review of a book about Warren McCulloch came up. They were friends. They actually had had been friends yet. It has something to do with their wife's. Yeah I think the lifestyle McCullough was not line with its a side story but but I guess the point you're making which I think is an I'd like us to take us back to seventy nine and the meeting with Jeff is and I think that that's true. Despite the story between humans the real factor that drove things then was the sudden available at a t of increasing cheap digital computer. And no longer the need to do this work that Rosenblatt and McCain and others had done having to wire together a bunch of analog circuits. That you couldn't reprogram to build system. Yeah I think that was a dead. End It for the very reason you gave. Which is that you know you. It's a special purpose device. That isn't good for anything else. And and really if you're trying to explore you need the flexibility of being able to try many ideas and that's in that really is a digital simulation allows you to do you see with Aaron seventy nine so by the time. What was the picture like? In this era in seventy nine that seems like a critical period. You had the facts. You had personal machines now in effect all personal ish machines so the interesting story. My first job was at Johns Hopkins University and I was Lucky. Enough to be awarded. The Presidential Science Award Young Investigator Award from you know the the government and along with that was a grant basically and was also matching so I had to get matching funds but because of that I was able to purchase ridge computers to enrich computers which had the power of VAC seven eighties. Ulta myself and for a while had more power competing power. Thenia Tire Computer Science Department. Google of one thousand nine hundred seventy nine. That's that's right but it really. You needed it because we were doing round the clock simulations that was the era of net. Talk which made a big splash and was tell us about net talk. Because it's it's a I I know what an inspiration to people who were an inspiration to me so tell us more about net dot net tool. Well it arose from a visit I made to Princeton and a graduate student. Charlie Rosenberg. Who was working with George Miller? Who's a very eminent cognitive scientists language area and and so Charlie was really enthusiastic about neural networks. And he asks he cannot come do a summer project site. Sure and you know he was studying language. So he's he wanted to do a language network and you know we cast around for a problem. You know a small network might be able to make some progress on what were the architectures of what year is this and what were the architects is available. It was eighty five. I think summer of eighty five and it was. You know at that. Time is really interesting because when I visited Princeton we were doing bonus jeans but by the time that he showed up. Jeff had with Dave Rummelhart had just broken through with backdrop which was an order of magnitude. Faster meant we could simulate a much bigger network. Well the problem we picked out was in phonology which is how do you pronounce words. And we we. I remember going to the library. And there was a two hundred and fifty page book with filled with rules and exceptions to the rules and rules for the exceptions because English is a very irregular language and there are a lot of different influences and that notion was kind of driven by logic that was the approach to language. Let's break language down into its which was the flavor of the decade double decade. Yeah no that was the era of Chomsky and Syntax. It was clear that you know. Rule based descriptions of something regular English was really complex and I actually remember Jeff visiting during the summer and telling us. We're crazy that this is much too difficult a problem. It's a real role problem and that we should MRIs pronounciation. So we're going to build a neural network to look it. Tax Restaurant pronounced text to speech text. Which is the problem is it. The world's first text to speech system no In fact there were systems. That are out there. We bought deck talk actually which allowed us to actually hear the output of talk which made it can't come alive.

Frank Rosenblatt Princeton Jeff Geoffrey Hinton Norbert Vena San Diego twitter Catherine Gorman Lawrence Charlie Rosenberg Dr Terence Subic N. G. M. C. H. S. Harvard Minsk Warren McCulloch Thenia Tire Computer Science D Johns Hopkins University Boehner Sheen
The Evolution of ML  and Furry Little Animals

Talking Machines

08:58 min | 9 months ago

The Evolution of ML and Furry Little Animals

"You are listening to talking machines Catherine Gorman Lawrence and Neil. We are again taping an episode in front of a live audience digitally recorded though on on talking machines. And if you want to be part of our live. Studio audience big quotes. You can follow us on twitter at Ti Okay. N. G. M. C. H. S. Or hit us up on the talking machines at gmail.com and our guest today for this interview on talking. Machines is Dr Terence. Annouce key doctors and thank you so much for taking the time to join us today. I really appreciate it Great to be here so we ask all of our guests the same question I. How did you get where you are? What's been your academic and industrial journey. You're also very involved in the reps conference. Tell US everything well. A wise man once told me that careers are only made retrospectively and I have no idea how he got here. There was no plan. It went through a sequence of stages starting with graduate school at Princeton in theoretical physics. From there when I finished that I for reasons that have to do with the field of physics. At the time which was a little bit more bummed I went into neuroscience so that was a post doc and then from there that's when I met. Geoffrey Hinton and had changed my life because we met him at a small seminar here in San Diego and set nineteen seventy nine. We hit it off and From that over the next few years you know blossoms the the Boehner Sheen and back prop and you know. The rest was history. Terry who you post talking with where you post talking in San Diego no no. This was a post doc at Harvard. Medical School in the Department of Neurobiology with Stephen Kofler who was widely considered to be the founder of modern neurobiology and It was an experimental post. Doc I actually recorded from neurons. Subic seventy nine. You mentioning physics. It was a little bit more bond a in some sort of connection modeling. That was also a very quiet period. That wasn't a lot going on it. Was this sort of age of classical. Ai Right you're absolutely right. This was in fact. It was the neural network winter. The seventies and it was primarily because of the failure of the perception. That's neat because you say failure of the percents on I read about that a lot. Do you really did fail. All was the men's ski paper little. What the mid ski books are in Minsk. Eighty books have killed it but was it a fair representation. Well you know it's interesting. I think that that's the myth that that book killed it but I actually think that there are other things going on and and Rosenblatt had died as well which seems pretty significant. Yes well He. He was a pioneer. But you have to understand that digital computers were regally primitive back. Then you know that even the most expensive you know the biggest computers you could buy. Don't have the power of your wristwatch today. Rosenblatt actually had to build an analog device. It a million dollars in today's dollars to build a analog device that had potentially otters driven by motors for the weight sums the learning. Wasn't it potentially because you know digital computers? Were good at logic but they were terrible. Doing a floating point is amazing so he built that at Cornell. Right that's right yeah Funded by the owner. Any case by by the time that we were getting started computers was the vaccine era. It was becoming possible. Do Simulations You know they were small-scale by today's standards but but really meant we could explorer in a way that Frank Rosenblatt couldn't so what you're saying around the perceptual and so just forbid of context for Central and sixty one. Is that right? It was fifty nine. I think it was the book but you know it was in that era of early sixty zero and so then there's this period where the digital computer actually wasn't powerful enough to do much and then digital kind of overtook and divinity but these analog machines would just now impractical from a point of view of expense. So you're saying it's less the book and more of a shift to the Digital Machine. That in those early days wasn't powerful enough to simulate the perception. Yes so I I have you know. I have a feeling that history will show that A. I was like the blind man looking under the Lamppost. His keys and someone came along and said where did you lose your keys He said well somewhere else. But this is the only place right can see. I was reading Donald BACI quote. I recently At the beginning of his book about the I which is just a fascinating area and I guess he spent a lot of his career and he did work in in the wool on radar and he was talking about the Radio Club. Which is these early Cybernet assist and the potential of the analog or digital computer to be what represented the brain and his perspective was he. He was sure it wasn't a digital computer and he wasn't sure it was an analog computer either and he thought it was kind of somewhere in between but it feels like that in between is what you're saying is that was the difficult bit to look and perhaps a police were able to look now. That's right I you know. It's I think it's being driven. This is true all science that what you cannot understand is is really determined by the tools that you have for making measurements for doing simulations in it's really only this modern era that has given us enough tools both to make progress with understanding how the brain works and also with a because of the fact that we have a tremendous amount of power now but just to go back to that early era. I think you know I once asked L. Annual you know who is at Carnegie Mellon and it was a time when Geoff Hinton was an assistant professor and I was at Johns Hopkins and I you know he was at the first fifty six meeting at Dartmouth or a I was born and I I said well. Why was it that you didn't look at the brain and for for inspiration and he said well we did. But there wasn't very much known about the at the time to help us out so we just had make doing our own and he's right. That was a era. You know the the fifties was kind of the the beginning of what we now understand about the signals in the brain. Actually potential synoptic potentials. So you know in a sense. What what he was saying was that we basically use the tools we have available the time which was basically computers but what they were good at. What were they good at? They were good at logic at rules. A binary programming. So that you know that was In a sense they were forced to do that. That's a really. WanNa come back to nine hundred seventy nine in a moment but this is an interesting context to that because of course. Vena initially was someone who spread across. Both these areas of Norbert Vena who was at mit founded cybernetics spread across both these areas of the analog and digital he did his PhD thesis on Russell and Whitehead's book but one thing I was reading about recently is there was a big falling out between Vina. I'm McCulloch Pitts. And it's sort of interesting. That Vena wasn't there at the I. E. T. in fifty six and I sometimes wonder was that more about personalities and wanting this sort of old guard to stay away because you always feel veto with someone who who bridge these worlds it. You know that's the fascinating story. I actually wrote a review of a book about Warren McCulloch came up. They were friends. They actually had had been friends yet. It has something to do with their wife's. Yeah I think the lifestyle McCullough was not line with its a side story but but I guess the point you're making which I think is an I'd like us to take us back to seventy nine and the meeting with Jeff is and I think that that's true. Despite the story between humans the real factor that drove things then was the sudden available at a t of increasing cheap digital computer. And no longer the need to do this work that Rosenblatt and McCain and others had done having to wire together a bunch of analog circuits. That you couldn't reprogram to build system. Yeah I think that was a dead. End It for the very reason you gave. Which is that you know you. It's a special purpose device. That isn't good for anything else. And and really if you're trying to explore you need the flexibility of being able to try many ideas and that's in that really is a digital simulation allows you to

Frank Rosenblatt Geoffrey Hinton San Diego Norbert Vena Twitter Catherine Gorman Lawrence Dr Terence Subic N. G. M. C. H. S. Harvard Minsk Boehner Sheen Warren Mcculloch Princeton Cornell Donald Baci Terry Mcculloch Pitts
"frank rosenblatt" Discussed on Trailblazers with Walter Isaacson

Trailblazers with Walter Isaacson

12:40 min | 1 year ago

"frank rosenblatt" Discussed on Trailblazers with Walter Isaacson

"It one of the most memorable scenes in movie history and for most viewers, who was a startling, and even frightening introduction to the world of artificial intelligence the scene. Occurs in Stanley, Kubrick's nineteen sixty eight groundbreaking film two thousand and one A Space Odyssey. Two astronauts are taking a space law. Aboard a space station bound for Jupiter, but the Howell nine thousand computer, that's in charge of the station refuses to let them back in. I'm sorry, Dave. I'm afraid I can't do that. Turns out that how was a machine with a very strong survival instinct. It had discovered that the astronauts were planning to disconnect it after they learned, in might of improperly reported a fault in the spacecraft's communication antenna, howls response was one of human instinct survival, and that's what made it so terrified the sheen to supposed to do what they were told after one of the astronauts managed to get back into the space craft he pulled the plug on how the saving the mission. While movie goers, marveled at house cognitive skills. They may have also taken another message home with them from the theater, be afraid be very afraid of super smart computers, who think and behave like humans, of course in nineteen sixty eight there were no computers. I could perform even a fraction of the functions that how was capable of computer generated speech, and facial recognition natural language processing, automated reasoning, and playing chess at a very high level or all still in the realm of science fiction today. Those functions are at our fingertips are pocket-sized digital devices deliver that and much more. Thanks to incredible advances in a field of research that few people at the time, have ever heard of artificial intelligence or a. And now will once again wondering what could happen when machines, the cheese which known as artificial genuine teller or super intelligence when they become smarter than us, while it's likely still decades away most experts agree. It will eventually happen what they don't agree on is what will mean for mankind. Would Mark the beginning of an exciting new world or environmental destruction can be reversed and deadly diseases, finally conquered or as a great physicist. Stephen hawking predicted Mark the end of civilization as we know it. I'm Walter Isaacson. And you're listening to Trail Blazers an original podcast from Dell technologies. I'm sorry, Walter. I'm afraid I can't do that. Computer is on the job around the clock computer and be called a kind of brain, efficient, computerized with a sleek, beauty, all its own the amazing machines and gadgets at almost seem to think for themselves, and you breed of computer that will test the engine, you ity both man and machine. One of the people Stanley Kubrick, turn to for advice, when writing the script for two thousand and Ron space. Odyssey was an American cognitive. Scientists may Marvin mentality at the time meant sqi was a professor at the MIT artificial intelligence lab, which he had helped to found a decade earlier along with AI pioneer John McCarthy. In fact, it was McCarthy, who I coined the term artificial intelligence, Marvin Mincy was also part of a small group of scientists that McCarthy brought together at Dartmouth College in nineteen fifty six to discuss this exciting new field of computer research for the first time. Jerry Kaplan teaches the social and economic impact of AI at Stanford University. And he's the author of the book, artificial intelligence, what everyone needs to know the interesting thing about the meeting was that nobody really came in with much of a preconceived notion about how one might actually perform the tasks that they were concerned about John McCarthy himself was a mathematician, who is very strong in mathematical logic, and his high podcasts, was that mathematical logic in reasoning was the basis for human intelligence. And so they began to explore programs that contain for example if then rules that perform certain kinds of logical inference in game playing if this happens, then I should do that. And their thesis was that if they could just do this well enough that they would be able to recreate many of the higher level, cognitive functions to the human mind. Hovering in the background with McCarthy and the others at the Dartmouth meeting with the challenge posts, six years, earlier, by the brilliant, British mathematician, and computer scientists Alan turing in a paper on computer intelligence touring. It proposed a test of machines. Ability to exhibit, intelligent behavior, that was indistinguishable from that of a human could a machine engage in natural language conversation. So sophisticated that a neutral third party wouldn't be able to tell whether it's a computer or human speaking. In other words could computers? Think touring prediction. Was that by the year two thousand the answer would be? Yes today. Millions of people interact with AI devices. Let's friendly names like Alexa, seething over a I can do simultaneous translation guide our cars and recognize images. But does all of that add up to real intelligence? What we've seen is two things. A no computer program is really passed the turing tests. And also think we've realized over the last fifty sixty years that the turing test more than anything is a test of human gullibility Orrin Etzion. He is the CEO of the Allen institute for artificial intelligence and Seattle and professor of computer science, at the university of Washington, if I were to administer the turing test, the first thing, I would do with a putative intelligence or is, I would give it the SAT's, I would ask to write an essay, I would give it a, an AP examined biology, and before even having a chat, I would grade those exams to make sure that I'm actually dealing with an intelligent entity. And then we could have a child about wines about my family and the kinds of things that computers have shown themselves to sometimes woodwinked us into thinking that they're human, like. The idea of building machine with human like qualities dates back long before Alan turing. In sixteen thirty seven the great French philosopher Rene Descartes speculated that it might be possible to build an automatic on that can imitate an animal, but he felt that humans contain two critical features that machines could never duplicate language and reason a machine might be able to perform a particular task better than a human, but it could not take what it learned from that experience and apply. It to other problems nearly four hundred years later, it seems that they caught was remarkably pression natural language processing and non logical reasoning remain, two of the biggest challenges confronting AI researchers as they try to build human, light qualities into the machines. Of course, there's been significant progress in nineteen Ninety-seven IBM's, deep blue beat world chess champion Garry, Kasporov checkmate, Gary, though, some argue that victory was more of a result of brute force computing, than artificial intelligence in two thousand eleven another IBM computer Watson won the TV game show jeopardy. And then in twenty sixteen more than two hundred million people worldwide watched as a machine Nayed alpha girl defeated the world champion of the board game, gove and four out of five matches those computers can play the game. They were programmed to play very well. But they can't do anything else. And that is why Aren Etzion he says we shouldn't read too much. Much into the success of these machines. There's an inherent paradox in which a lot of people don't get things that are super hard for people like playing championship level. Go turn out to be quite easy for the machine. Right. And then on the other hand things that are quite simple for person like crossing the street understanding a simple children's story are things that are actually very difficult for the machine. So people see these remarkable successes in these very narrow tasks like chess, or go, and they extrapolate from that, that I can do amazing things, and they I can do amazing things, but in very narrow arenas like Mr. spot in the Star Trek TV series those early. Scientists thought mathematical logic was best way to recreate human intelligence. But that approach quickly proved to be far too limited human intelligence. Involves making judgments, and finding creative solutions to problems that are not always strictly logical. So at the same time as scientists were meeting at Dartmouth another researcher was working on a radically different approach one that in recent years has taken us a lot closer to the goal of artificial genuine talents. It involves recreating. The brain's normal networks inside of a computer to imitate the human thought process today. These machine learning networks aware, most of the buzz around AI is coming from. It was first proposed in the nineteen fifties by Cornell psychologist, named Frank Rosenblatt. Jerry Kaplan, rosenblatts, approach was a rival approach, and it simply wasn't regarded that well by the rest of the community partially because of the outlandish claims that Rosenblatt had made four his invention and partially because the computers at the time, simply weren't powerful enough to perform the kinds of functions that modern machine learning programs can do with the proliferation of digital data that we have available in the world today. So basically, there was a mismatch between the technology and tools are available. And now we have tools that are capable of using rosenblatts machine learning neural network approach to solve all these problems where the original pro taken by the founders of the field in nineteen fifty-six the approach that they were taking simply wasn't adequate for that to solve those kinds of problems. Before

John McCarthy Stanley Kubrick Jerry Kaplan Walter Isaacson professor of computer science Howell Dave Stephen hawking Frank Rosenblatt Trail Blazers Alexa Rene Descartes Odyssey IBM physicist
"frank rosenblatt" Discussed on Brain Science with Ginger Campbell, MD: Neuroscience for Everyone

Brain Science with Ginger Campbell, MD: Neuroscience for Everyone

01:48 min | 1 year ago

"frank rosenblatt" Discussed on Brain Science with Ginger Campbell, MD: Neuroscience for Everyone

"I'm asking that question is because it leads into the comparison between the goals of a in the goals of neuroscience because I think they're really different. Yeah. Yeah. So the goals of a are really kind of in the problem solving direction. Right. Well, that's one aspect because other goal of AI. I mean think back to that perception and what Frank Rosenblatt wrote. This was his attempt to start studying information flow in the brain. And how the brain actually accomplish thing things. So he was directly trying to study brain processes there. So that's another. I don't think you can divorce AI from that completely. But if you take somebody like mint sqi, didn't he just evisceration him over that? I mean like that's not what we're about over whether we're trying to study brains. Yeah. Well, you know, I don't know men's key himself was really interested in cognition. Here's a cognitive scientist as well. And had a lot. Of early influential ideas. So I'm not sure I'm not sure about that. That's a good question. That's fair because I don't know too much about it either will ginger what would you say the goal of neuroscience is to figure out how the brain really works. Yeah. Okay. So well, I think yours is safer than mine. Probably. So I I think it's figure out how brains implements, you know, all of our experiences and thoughts and actions and bodily and intercept of processes and in short that means figure out how the brain works. Really works. Yeah. So I like your your definition better. I suppose. Gonna break into the interview here to remind you that your support is vital to the ongoing success of brain science..

Frank Rosenblatt scientist
"frank rosenblatt" Discussed on Brain Science with Ginger Campbell, MD: Neuroscience for Everyone

Brain Science with Ginger Campbell, MD: Neuroscience for Everyone

04:04 min | 1 year ago

"frank rosenblatt" Discussed on Brain Science with Ginger Campbell, MD: Neuroscience for Everyone

"So let's talk a little bit about this whole AI neuro science thing, let's start out with well. How do they inform each other or fail to inform? Each other. Do you wanna start with just maybe giving us a primer of some of the important terms and principles that we need to be able to even talk about artificial intelligence that would take a week, I suppose, but if somebody's coming to your show, what kind of terms do you sort of assume they already understand. It's a changing assumption. Right. Because in the early days of the show, I would define all the terms. And I've I've found myself leaving that a lot of it to the audience assuming that they're coming in with some of this knowledge. A lot of what we talk about on the show is deep learning gladly because there has been this recent just explosion in deep learning in all sorts of industry and in neuro science. So a lot of my guests on the. Show and we explore other topics as well. But a lot of the guests on the show are exploring the the relationship between deep learning and neuroscience, and what they can gain from each other essentially, so this is a heavy topic on the show. I could give you sort of a deep learning background in the story of how that came to be if you'd like that would be a good place to start if we get a basic concept of deep learning, and then maybe we could talk about where deep learning fits into the bigger picture beautiful. Okay. So this is going to be a very abridged version of the sort of the history of depleting. And and just what it is what it means. But there's a really good book that I recommend to your listeners, and I interviewed Terry Santo ski who wrote this book called the deep learning revolution. And it really lays out the story of how we got to this point in deep learning from its founding ones, and Terri knows this stuff because he was involved in like so many steps along the way and. So many people involved in it were either from his lab or if he collaborated with them, so and it really lays this story out. Well, so I just wanted to recommend that book to your listeners. Yeah. That's on. My I wish I had time to read it list. I haven't gotten around to well, it's well written in tears, a good teacher too. So anyway, I'll send you a copy and make in guilt you into reading it. Okay. And I'll put links to it in my show notes for sure great. So just to set the context and give a little bit of background before really dive into deep learning in nineteen fifty six the term artificial intelligence rather was coined by John McCarthy during the summer conference at Dartmouth College, and this would become the famous conference called the Dartmouth College summer AI conference. This is after Walter Pitts and Warren McCulloch developed what came to be known as a McCulloch Pitts neurons, which basically is the precursor to the artificial neuron units that are used in deeply. Earning networks these days. It was also after Alan turing set the stage for computers, as we know them and machine learning as we know it, but at this time when this conference happened, most engineering and computer based solutions to solving intelligence problems were based on logical steps and manipulating symbols that humans would manually build in to the systems. Now, really where I wanna start is with what's called the perception. And I wanna start here because basically perception is deep learning without the deep part. So if we understand a perception will it's really easy to understand the deep part of the learning of you could call it. Shallow learning, I suppose, everything that's not deep learning is shallow running. Okay. So Frank Rosenblatt invented the perception in nineteen fifty seven and basically this is right. When you were being conceived. Ginger. No, I was born in nineteen fifty five fifty five. So you are maybe almost on the swing sets by this point. Not that precocious..

Dartmouth College Walter Pitts Frank Rosenblatt Terry Santo Warren McCulloch Terri John McCarthy
"frank rosenblatt" Discussed on American Innovations

American Innovations

07:04 min | 2 years ago

"frank rosenblatt" Discussed on American Innovations

"Slash. AI that's ZipRecruiter dot com. Slash AI ZipRecruiter dot com slash AI and you should stay tuned through the end of this episode to hear about ZipRecruiter's culture of innovation, sip recruiter, the smartest way to hire. From wondering, I'm Steven Johnson, and this is American innovations. This is fourth episode in our series about artificial intelligence and the thinking machine and the first three episodes. We follow the journey of computer scientists, dreamed of breathing, the best of human intellectual abilities into machines. We followed the epic years long battle between IBM and chess world champion Garry casper in our last episode explored how the Pentagon's investment in new technology eventually led to the creation of the iphones, a personal assistant. Siri this episode is about a leak in the power of artificial intelligence. The caused some to raise the alarm about possible dangers ahead. It's called machine learning and it began right around the time. Siri launched late in two thousand eleven Google the term, and you will see pages of articles forums, academic papers, research and blogs about the topic. But at its essence, it boils down to this machine learning is providing information about the world or a problem to a computer. So it's understanding, improves over time. You can think of it being something like how a human can learn through practice or seeing examples over and over. It's an endeavor that excites the biggest names in tech. This is episode four, I learn, therefore I am. It's early in two thousand eleven Jeff dean. A veteran programmer at Google is browsing one of the companies, well-stocked, snack kitchens, their amongst the smoothies and fancy coffee. He gets talking with quietspoken, Stanford professor named Andrew ING. The conversation will change the lives of both of them and all of us to so Andrew. What are you working on these days? I'm running a new a ohi project, Google ax, you know, the lab, the media likes to call it secretive moonshot factory because no one knows where it is. That sounds exciting. What's it about? Well, we're training neural networks. You know, computer systems kind of modeled on the human brain, no way I wrote my undergrad thesis on neural networks back in nineteen ninety. I got. They went out of fashion a long time ago because no one could get them to work very well. I know I couldn't. I think we are going to get them to work this time, the bay. Ideas mostly the same, but it's just that we have so much more data now than we used to much more powerful computers to train them on. So what are you teaching them to make sense of photos. We scrape together millions of images from YouTube to try and teach them to recognize images out there in the world you should buy sometime we call it project Marvin after Marvin Minsk. AI pioneer at MIT. Dean does drop by visit project Marvin, and before long he spending a lot of time with Andrew ING and a small team at Google x, the idea of getting neural networks properly working after all these years peaks curiosity and the way is going about it is perfect for dean. Dean is widely recognized as one of silicon valley's most creative influential programmers in the early two thousands. He created the huge distributed software systems that harnessed together countless servers to power Google search engine and other services. Now he's working with anger, get thousands of computers to work together to power, normal networks, a few months after and dean's meeting project. Marvin has been renamed Google brain, and the team starts an experiment that will help change the path of Google and technology all over the planet. It involves only a few simple steps. I the Google brain team clips images, random from ten million YouTube videos. Next they feed them into neural networks offer running on sixteen thousand powerful processors with one billion connections between simulated neurons and then believe it running for a week to browse the images and try to find Packers. When they return, they discover the software has learned to detect the most common objects on YouTube all by itself, human faces and body parts and cats on June twenty. Fifth, two thousand twelve the results land on the front page of the New York Times business section. The headline is a riddle, how many computers to identify a cat. Sixteen thousand dean tells a reporter that the team never told the computer. What a cat was. It came up with the concept all on its own, like a human. Learn to identify something through visual repetition. The project convinces Google, it needs to invest more a lot more into neural networks. One of the first people they turn to is Jeff hin, a lean dry witted, British professor living in Canada hidden. It helped keep alive. The idea of neural networks for decades during which they didn't see much use at all way back in nineteen sixty psychologist. Frank Rosenblatt had built an artificial neural network out of electronics called the Mark one percent Tron. It took up almost an entire room and it was attached to a camera and could learn to distinguish different letters of the alphabet. Rosenblatt predicted future versions would identify aircraft for the military and take voice commands, but then Marvin Mincy an MIT. The early pioneer we met in episode. One wrote a book that claimed to prove neural networks would never be very powerful interests and progress on the idea mostly. Pulled out apart from a few stubborn believers. Hinton had been obsessed with neural networks for a long time in the nineteen seventies his PHD supervisor at the university of Edinburgh, advise the straggling haired hidden that working on neural networks was a waste of time, but he refused to give up. Hinton was a firm believer that computers could learn and develop intuition, not just process the world using built in rules neural networks, even garnered notice in popular culture in nineteen Ninety-two the movie Terminator two judgment day. Arnold Schwarzenegger playing the t eight hundred robot gets quizzed about his brain by John. Connor the kid. He traveled back in time to save. Very staff that you having been program with. So you can be. You know more human and that's Detroit all the time. My see you as neural net process of a learning computer to more contact I have with humans. The more I learn.

Google Jeff dean Andrew ING YouTube Marvin Frank Rosenblatt MIT Siri Steven Johnson IBM professor ZipRecruiter Detroit Arnold Schwarzenegger Garry casper New York Times
"frank rosenblatt" Discussed on Impact Theory with Tom Bilyeu

Impact Theory with Tom Bilyeu

02:58 min | 2 years ago

"frank rosenblatt" Discussed on Impact Theory with Tom Bilyeu

"Is very influential you can sign up for on the homepage of kurzweil while a i dot net perfect and then my final question is what is the impact that you want to have on the world my massively transformative purpose which goes back more than fifty years to nineteen sixty two when i was fourteen and i met with the marvin minsk you became my mentor fifty five years and frank rosenblatt really started the connection at school is to artificial intelligence to amplify our own intelligence and to enable us to solve problems that we couldn't otherwise i'll it's only intelligence at enables us to make progress if it went for our nate intelligence we'd still be writing on cable all's we wouldn't be doing that and we've made tremendous progress i mean if you read what life was like even two hundred years ago re thomas hobbes describes life is short buddhist disaster prone poverty filled disease filled extremely harsh let alone a thousand years ago people tend to emancipates the past but we've made life immeasurably better because of applying our intelligence to solving one problem after another and if we had more intelligence we could do more of that and that's that's been my passion holies for the last fifty years awesome thank you so much shit i guys this is somebody who's been making predictions for a very long time been doing incredible things for a long time so when you dive into his world there's going to be an immeasurable amount of stuff to see there and the one thing that i hope came across in this interview and that you will see without question as you research more into him is there is a beautiful optimism to what he's trying to pull off like he said it can't be some sort of overly simplistic trite like everything is going to be okay it's really about being driven to figure out and solve the problems of yourselves my favorite story about him is that when faced with a heart condition he looked at it and said well if it had been something more complicated i would have just had to invent a solution for that and when you approach the world like that and have the optimism the fortitude and the persistence to pursue the things that you're passionate about in order to turn them into something that is actually usable you get what i think is the ultimate way forward and when people look at him and try to classify ray it really is as the man who is ushering in the future and i don't think that we can be in any more capable hands guys if you haven't already be sure to subscribe and until next time my friends be legendary take care everybody thank you so much for listening and if this content is delivering value to you police go to tunes go to stitcher rate and review us that helps us build this community and that is what we're all about right now buildings community as big as we can to help as many people as we can deliver as much value as possible and you guys rating and reviewing really helps with that all right guys thank you again so much and until next time my friends you legendary take care.

kurzweil fifty years two hundred years fifty five years thousand years